[More discussion on targeted SDV can be found in the latest version of Applied Clinical Trials. Sponsors and project managers should develop SDV strategies for each trial that comply with regulatory requirements and accommodate the size, complexity, design, and purpose of the trial.  One hundred percent SDV, the comparison of each data point on every case report form (CRF) to subject medical records, may not be appropriate for most large, multi-center trials. Targeted SDV—the verification of critical trial data, including study endpoints—has the potential to improve safety oversight, data quality, regulatory compliance, protocol adherence, and overall trial validity while reducing costs and the time to database lock for large, multi-center trials.]

We’ve had it! Enough with 100% SDV. It’s simply not needed and a waste of time and money.  The CenterWatch Monthly ran an article in their February issue on the high cost and questionable impact of 100% source data verification (SDV). Source data verification checks that the data collected on a research study can be verified by looking at a primary source, such as a medical record, in essence, checking for consistency and accuracy in transcribing data from one place to another.

Despite no evidence that it improves data quality, a highly conservative interpretation of FDA regulations regarding data monitoring has led to 100% source data verification becoming standard industry practice. FDA regulations do not require monitors to check every source data point at each and every investigative site. Plus, this approach is costly, time-consuming and diverts attention and resources from more critical clinical trial activities. Yet, many drug companies believe its best way to ensure the validity and integrity of clinical trial data.

So, what is the driving force behind this practice? When validating clinical trial data, the old adage of quality trumping quantity SHOULD ring true, but when it comes to data monitoring it often does not. Many clinical trial operators don’t understand data qualification and data verification are not synonymous. Without the proper people, SOPs, and technology in place, many clinical trial sponsors believe 100% SDV is the only way to ensure clean data – they’re being reactive rather than proactive.

As companies have begun to take a hard look at all trial costs (monitoring is one of, if not the largest expenses), they are revisiting monitoring processes in search of more efficient means of leveraging prior site performance in hopes of more effective trial management.

Beginning with the end in mind makes for a controlled sampling methodology for selecting source data to be verified in any clinical trial.  Improved technology adoption and clinical operations processes are now making it possible to redefine the role of the CRA, while at the same time reducing stress, decreasing costs and increasing clinical trial oversight. At Clinipace Worldwide, we’ve adopted a methodology called Just-in-Time Monitoring that is dramatically changing the way we interact with our trial sites.

With just-in-time monitoring, a CRA can view a variety of data elements remotely and immediately detect any outlying or troublesome data. An improved level of visibility into the data means that small problems can be spotted early and don’t have the opportunity to develop into larger problems that might slow the study or endanger a patient.

Remote monitoring that includes complete data visibility makes it possible to implement corrective actions and get nearly immediate feedback on the effectiveness of those actions. Slight alterations in course early on avoid the need for gross adjustments later.

Constant monitoring of trial data and site performance also help to make visits more efficient and purposeful. Instead of having to spend days looking at everything, a CRA is able to pinpoint exact areas that need to be addressed while on-site. This allows visits to become more strategic in nature, where the CRA and site personnel work together to solve specific problems and set future goals.